Finding the location and concentration of groundwater contaminant sources typically requires the solution of an inverse problem. A parallel hybrid optimization framework that uses genetic algorithms (GA) coupled with local search approaches (GA-LS) has been developed previously to solve groundwater inverse problems. In this study, the identification of an emplaced source at the Borden site is carried out as a test problem using this optimization framework by using a Real Genetic Algorithm (RGA) as the GA approach and a Nelder–Mead simplex as the LS approach. The RGA results showed that the minimum objective function did not always correspond to the minimum solution error, indicating a possible non-uniqueness issue. To address this problem, a procedure to identify maximally different starting points for LS is introduced. When measurement or model errors are non-existent or minimal it is shown that one of these starting points leads to the true solution. When these errors are significant, this procedure leads to multiple possible solutions that could be used as a basis for further investigation. Metrics of mean and standard deviation of objective function values was adopted to evaluate the possible solutions. A new selection criterion based on these metrics is suggested to find the best alternative. This suggests that this alternative generation procedure could be used to address the non-uniqueness of similar inverse problems. A potential limitation of this approach is the application to a wide class of problems, as verification has not been performed with a large number of test cases or other inverse problems. This remains a topic for future work.